This document discusses how repositories can support users in achieving FAIR (Findable, Accessible, Interoperable, Reusable) principles for data management and stewardship. It explains how repositories can help with each of the FAIR principles by providing globally unique identifiers, tools for rich metadata, indexing metadata, standardized access protocols, and support for licensing, attribution and community standards. The document also discusses challenges repositories face in achieving full interoperability and some potential areas of agreement like common metadata and accessibility APIs.
Presentation by Luiz Olavo Bonino, Dutch Techcentre & Vrije University Amsterdam.
As one of the organisations present at the Lorentz workshop in January 2014 where the concept of FAIR Data has been created, the Dutch Techcentre for Life Sciences has, since then, worked on a number of solutions to support the adoption and dissemination of the FAIR Data Principles. This presentation presents the ecosystem on how to support FAIR data.
Towards FAIR principles for research software @ FAIR Software Session, Nation...annalenalamprecht
Slides of a presentation about our paper "Towards FAIR Principles for Research Software" (https://doi.org/10.3233/DS-190026) that I gave at the Dutch National eScience Symposium 2019 in Amsterdam (https://www.esciencesymposium2019.nl/).
Abstract:
The FAIR Guiding Principles, published in 2016, aim to improve the findability, accessibility, interoperability and reusability of digital research objects for both humans and machines. Until now the FAIR principles have been mostly applied to research data. The ideas behind these principles are, however, also directly relevant to research software. Hence there is a distinct need to explore how the FAIR principles can be applied to software. In this work, we aim to summarize the current status of the debate around FAIR and software, as basis for the development of community-agreed principles for FAIR research software in the future. We discuss what makes software different from data with regard to the application of the FAIR principles, and which desired characteristics of research software go beyond FAIR. Then we present an analysis of where the existing principles can directly be applied to software, where they need to be adapted or reinterpreted, and where the definition of additional principles is required. Here interoperability has proven to be the most challenging principle, calling for particular attention in future discussions. Finally, we outline next steps on the way towards definite FAIR principles for research software.
Presentation by Luiz Olavo Bonino, Dutch Techcentre & Vrije University Amsterdam.
As one of the organisations present at the Lorentz workshop in January 2014 where the concept of FAIR Data has been created, the Dutch Techcentre for Life Sciences has, since then, worked on a number of solutions to support the adoption and dissemination of the FAIR Data Principles. This presentation presents the ecosystem on how to support FAIR data.
Towards FAIR principles for research software @ FAIR Software Session, Nation...annalenalamprecht
Slides of a presentation about our paper "Towards FAIR Principles for Research Software" (https://doi.org/10.3233/DS-190026) that I gave at the Dutch National eScience Symposium 2019 in Amsterdam (https://www.esciencesymposium2019.nl/).
Abstract:
The FAIR Guiding Principles, published in 2016, aim to improve the findability, accessibility, interoperability and reusability of digital research objects for both humans and machines. Until now the FAIR principles have been mostly applied to research data. The ideas behind these principles are, however, also directly relevant to research software. Hence there is a distinct need to explore how the FAIR principles can be applied to software. In this work, we aim to summarize the current status of the debate around FAIR and software, as basis for the development of community-agreed principles for FAIR research software in the future. We discuss what makes software different from data with regard to the application of the FAIR principles, and which desired characteristics of research software go beyond FAIR. Then we present an analysis of where the existing principles can directly be applied to software, where they need to be adapted or reinterpreted, and where the definition of additional principles is required. Here interoperability has proven to be the most challenging principle, calling for particular attention in future discussions. Finally, we outline next steps on the way towards definite FAIR principles for research software.
A presentation of the Dutch Techcentre for Life Sciences FAIR Data ecosystem given at the BlueBridge workshop, a pre-event of the Research Data Alliance's 9th Plenary
A very brief presentation on the FAIR data principles at a workshop on Traits data, especially picking out the challenges specific to this field (although the slides don't reflect this ;-) 17 May 2018
AFAIR in Astronomy Research - Slides. In this webinar ARDC is partnering with the ADACS project to explore the FAIR data principles in the context of Astronomy research and the ASVO and IVOA as a community exemplars of the implementation of the FAIR data principles.
These slides from: Keith Russell (ARDC): Looking at FAIR
In this talk Keith will provide an overview of the FAIR principles and how it was used in astronomy before it became official. He will conclude the talk by discussing what other disciplines can learn from their approach.
Towards metrics to assess and encourage FAIRnessMichel Dumontier
With an increased interest in the FAIR metrics, there is need to develop tools and appraoches that can assess the FAIRness of a digital resource. This talk begins to explore some ideas in this space, and invites people to participate in a working group focused on the development, application, and evaluation of FAIR metric efforts.
The FAIR Data Principles (Findable, Accessible, Interoperable, and Reusable), published
on Scientific Data in 2016, are a set of guiding principles proposed by a consortium of
scientists and organizations to support the reusability of digital assets. It has since been
adopted by research institutions worldwide. The guidelines are timely as we see
unprecedented volume, complexity, and creation speed of data.
Access to biomedical data is increasingly important to enable data driven science in the research community.
The Linked Open Data (LOD) principles (by Tim Berner-Lee) have been suggested to judge the quality of data by its accessibility (open data access), by its format and structures, and by its interoperability with other data sources.
The objective is to use interoperable data sources across the Web with ease.
The FAIR (findable, accessible, interoperable, reusable) data principles have been introduced for similar reasons with a stronger emphasis on achieving reusability.
In this presentation we assess the FAIR principles against the LOD principles to determine, to which degree, the FAIR principles reuse LOD principles, and to which degree they extend the LOD principles.
This assessment helps to clarify the relationship between both schemes and gives a better understanding, what extension FAIR represents in comparison to LOD.
We conclude, that LOD gives a clear mandate to the openness of data, whereas FAIR asks for a stated license for access and thus includes the concept of reusability under consideration of the license agreement.
Furthermore, FAIR makes strong reference to the contextual information required to improve reuse of the data, e.g., provenance information.
According to the LOD principles, such meta-data would be considered interoperable data as well, however, the requirement of extending of data with meta-data does indicate that FAIR is an extension of the LOD (in contrast to the inverse).
The FAIR principles have been introduced as a guideline for good scientific data stewardship. They have gained momentum at a management level and are now for example part of the project template for EU Horizon 2020 projects. This raises the question what research groups and projects can do to implement them. Hugo Besemer will introduce the ideas behind the FAIR principles.
OSFair2017 workshop | Monitoring the FAIRness of data sets - Introducing the ...Open Science Fair
Elly Dijk & Peter Doorn present the DANS approach to FAIR metrics
Workshop title: Open Science Monitor
Workshop overview:
Which are the measurable components of Open Science? How do we build a trustworthy, global open science monitor? This workshop will discuss a potential framework to measure Open Science, including the path from the publishing of an open policy (registries of policies and how these are represented or machine read), to the use of open methodologies, and the opening up of research results, their recording and measurement.
DAY 2 - PARALLEL SESSION 5
A presentation of the Dutch Techcentre for Life Sciences FAIR Data ecosystem given at the BlueBridge workshop, a pre-event of the Research Data Alliance's 9th Plenary
A very brief presentation on the FAIR data principles at a workshop on Traits data, especially picking out the challenges specific to this field (although the slides don't reflect this ;-) 17 May 2018
AFAIR in Astronomy Research - Slides. In this webinar ARDC is partnering with the ADACS project to explore the FAIR data principles in the context of Astronomy research and the ASVO and IVOA as a community exemplars of the implementation of the FAIR data principles.
These slides from: Keith Russell (ARDC): Looking at FAIR
In this talk Keith will provide an overview of the FAIR principles and how it was used in astronomy before it became official. He will conclude the talk by discussing what other disciplines can learn from their approach.
Towards metrics to assess and encourage FAIRnessMichel Dumontier
With an increased interest in the FAIR metrics, there is need to develop tools and appraoches that can assess the FAIRness of a digital resource. This talk begins to explore some ideas in this space, and invites people to participate in a working group focused on the development, application, and evaluation of FAIR metric efforts.
The FAIR Data Principles (Findable, Accessible, Interoperable, and Reusable), published
on Scientific Data in 2016, are a set of guiding principles proposed by a consortium of
scientists and organizations to support the reusability of digital assets. It has since been
adopted by research institutions worldwide. The guidelines are timely as we see
unprecedented volume, complexity, and creation speed of data.
Access to biomedical data is increasingly important to enable data driven science in the research community.
The Linked Open Data (LOD) principles (by Tim Berner-Lee) have been suggested to judge the quality of data by its accessibility (open data access), by its format and structures, and by its interoperability with other data sources.
The objective is to use interoperable data sources across the Web with ease.
The FAIR (findable, accessible, interoperable, reusable) data principles have been introduced for similar reasons with a stronger emphasis on achieving reusability.
In this presentation we assess the FAIR principles against the LOD principles to determine, to which degree, the FAIR principles reuse LOD principles, and to which degree they extend the LOD principles.
This assessment helps to clarify the relationship between both schemes and gives a better understanding, what extension FAIR represents in comparison to LOD.
We conclude, that LOD gives a clear mandate to the openness of data, whereas FAIR asks for a stated license for access and thus includes the concept of reusability under consideration of the license agreement.
Furthermore, FAIR makes strong reference to the contextual information required to improve reuse of the data, e.g., provenance information.
According to the LOD principles, such meta-data would be considered interoperable data as well, however, the requirement of extending of data with meta-data does indicate that FAIR is an extension of the LOD (in contrast to the inverse).
The FAIR principles have been introduced as a guideline for good scientific data stewardship. They have gained momentum at a management level and are now for example part of the project template for EU Horizon 2020 projects. This raises the question what research groups and projects can do to implement them. Hugo Besemer will introduce the ideas behind the FAIR principles.
OSFair2017 workshop | Monitoring the FAIRness of data sets - Introducing the ...Open Science Fair
Elly Dijk & Peter Doorn present the DANS approach to FAIR metrics
Workshop title: Open Science Monitor
Workshop overview:
Which are the measurable components of Open Science? How do we build a trustworthy, global open science monitor? This workshop will discuss a potential framework to measure Open Science, including the path from the publishing of an open policy (registries of policies and how these are represented or machine read), to the use of open methodologies, and the opening up of research results, their recording and measurement.
DAY 2 - PARALLEL SESSION 5
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Apresentação na mesa de conversa com pesquisadores sobre acesso aberto, diretrizes e elaboração de planos de gestão de dados da UNIRIO no dia 14 de junho de 2018.
Presentation by Luiz Olavo Bonino about the current state of the developments on FAIR Data supporting tools at the Dutch Techcentre for Life Sciences Partners Event on November 3-4 2016.
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2. FAIR PRINCIPLES
Findable:
F1. (meta)data are assigned a globally unique and persistent
identifier;
F2. data are described with rich metadata;
F3. metadata clearly and explicitly include the identifier of the
data it describes;
F4. (meta)data are registered or indexed in a searchable
resource;
Accessible:
A1. (meta)data are retrievable by their identifier using a
standardized communications protocol;
A1.1 the protocol is open, free, and universally
implementable;
A1.2. the protocol allows for an authentication and
authorization procedure, where necessary;
A2. metadata are accessible, even when the data are no longer
available;
Interoperable:
I1. (meta)data use a formal, accessible, shared, and broadly
applicable language for knowledge representation.
I2. (meta)data use vocabularies that follow FAIR principles;
I3. (meta)data include qualified references to other
(meta)data;
Reusable:
R1. (meta)data are richly described with a plurality of accurate and
relevant attributes;
R1.1. (meta)data are released with a clear and accessible data
usage license;
R1.2. (meta)data are associated with detailed provenance;
R1.3. (meta)data meet domain-relevant community
standards;
https://www.nature.com/articles/sdata201618
3. FAIR DATA PRINCIPLES - METADATA
Findable:
F1. metadata are assigned a globally unique and persistent
identifier;
F2. data are described with rich metadata;
F3. metadata clearly and explicitly include the identifier of the
data it describes;
F4. metadata are registered or indexed in a searchable
resource;
Accessible:
A1. metadata are retrievable by their identifier using a
standardized communications protocol;
A1.1 the protocol is open, free, and universally
implementable;
A1.2. the protocol allows for an authentication and
authorization procedure, where necessary;
A2. metadata are accessible, even when the data are no longer
available;
Interoperable:
I1. metadata use a formal, accessible, shared, and broadly
applicable language for knowledge representation.
I2. metadata use vocabularies that follow FAIR principles;
I3. metadata include qualified references to other metadata;
Reusable:
R1. metadata are richly described with a plurality of accurate and
relevant attributes;
R1.1. metadata are released with a clear and accessible data
usage license;
R1.2. metadata are associated with detailed provenance;
R1.3. metadata meet domain-relevant community standards;
https://www.nature.com/articles/sdata201618
4. FAIR DATA PRINCIPLES – DATA/DIGITAL RESOURCES
Findable:
F1. data are assigned a globally unique and persistent
identifier;
F2. data are described with rich metadata;
F3. metadata clearly and explicitly include the identifier of the
data it describes;
F4. data are registered or indexed in a searchable resource;
Accessible:
A1. metadata are retrievable by their identifier using a
standardized communications protocol;
A1.1 the protocol is open, free, and universally
implementable;
A1.2. the protocol allows for an authentication and
authorization procedure, where necessary;
A2. metadata are accessible, even when the data are no longer
available;
Interoperable:
I1. data use a formal, accessible, shared, and broadly applicable
language for knowledge representation.
I2. data use vocabularies that follow FAIR principles;
I3. data include qualified references to other (meta)data;
Reusable:
R1. metadata are richly described with a plurality of accurate and
relevant attributes;
R1.1. metadata are released with a clear and accessible data
usage license;
R1.2. metadata are associated with detailed provenance;
R1.3. metadata meet domain-relevant community standards;
https://www.nature.com/articles/sdata201618
5. FAIR DATA PRINCIPLES – SUPPORT INFRASTRUCTURE
Findable:
F1. (meta)data are assigned a globally unique and
persistent identifier;
F2. data are described with rich metadata;
F3. metadata clearly and explicitly include the identifier
of the data it describes;
F4. (meta)data are registered or indexed in a searchable
resource;
Accessible:
A1. (meta)data are retrievable by their identifier using a
standardized communications protocol;
A1.1 the protocol is open, free, and universally
implementable;
A1.2. the protocol allows for an authentication and
authorization procedure, where necessary;
A2. metadata are accessible, even when the data are no
longer available;
Interoperable:
I1. (meta)data use a formal, accessible, shared, and
broadly applicable language for knowledge
representation.
I2. (meta)data use vocabularies that follow FAIR
principles;
I3. (meta)data include qualified references to other
(meta)data;
Reusable:
R1. (meta)data are richly described with a plurality of
accurate and relevant attributes;
R1.1. (meta)data are released with a clear and
accessible data usage license;
R1.2. (meta)data are associated with detailed
provenance;
R1.3. (meta)data meet domain-relevant community
standards;
https://www.nature.com/articles/sdata201618
6. REPOSITORIES ROLES IN FAIR
As services to store (and manage) digital objects (metadata, data, vocabularies,
ontologies, etc.
Provide facilities for their users to achieve higher levels of FAIRness
Can guarantee a minimal level of FAIRness independent of further users efforts on the content
As digital objects themselves
Should also observe the FAIR principles
At least FAIR metadata
Improve interoperability among repositories
7. REPOSITORIES SUPPORTING USERS TO ACHIEVE FAIR
Findable:
F1. (meta)data are assigned a globally unique and persistent identifier;
How?
Provide globally unique and persistent identifiers for the
submitted metadata and data
8. REPOSITORIES SUPPORTING USERS TO ACHIEVE FAIR
Findable:
F2. data are described with rich metadata;
How?
Help users to provide as rich metadata as possible to help
others to find their digital resources
10. REPOSITORIES SUPPORTING USERS TO ACHIEVE FAIR
Findable:
F3. metadata clearly and explicitly include the identifier of the data it describes;
How?
Automatically include the identifier of the target digital
object in its metadata
11. REPOSITORIES SUPPORTING USERS TO ACHIEVE FAIR
Findable:
F4. (meta)data are registered or indexed in a searchable resource;
How?
Index or facilitate the indexing of the metadata
12. REPOSITORIES SUPPORTING USERS TO ACHIEVE FAIR
Findable:
F4. (meta)data are registered or indexed in a searchable resource;
Example
13. REPOSITORIES SUPPORTING USERS TO ACHIEVE FAIR
Accessible:
A1. (meta)data are retrievable by their identifier using a standardized communications protocol;
A1.1 the protocol is open, free, and universally implementable;
A1.2. the protocol allows for an authentication and authorization procedure, where necessary;
How?
Provide accessibility on the Web, with security measures
when necessary
14. REPOSITORIES SUPPORTING USERS TO ACHIEVE FAIR
Accessible:
A2. metadata are accessible, even when the data are no longer available;
How?
Maintain metadata even when the target digital object is no
longer available
15. REPOSITORIES SUPPORTING USERS TO ACHIEVE FAIR
Interoperable:
I1. (meta)data use a formal, accessible, shared, and broadly applicable language for knowledge
representation.
How?
Serialize the metadata using a formal, accessible, shared
and broadly applicable knowledge representation language.
E.g., RDF/JSON-LD
16. REPOSITORIES SUPPORTING USERS TO ACHIEVE FAIR
Interoperable:
I2. (meta)data use vocabularies that follow FAIR principles;
How?
As vocabularies can also be stored in repositories, they
should also achieve a level of FAIRness
17. REPOSITORIES SUPPORTING USERS TO ACHIEVE FAIR
Interoperable:
I3. (meta)data include qualified references to other (meta)data;
How?
Repositories can provide facilities to support the inclusion of
qualified references to other (meta)data, e.g., semantic
annotations.
18. REPOSITORIES SUPPORTING USERS TO ACHIEVE FAIR
Interoperable:
I3. (meta)data include qualified references to other (meta)data;
Example
19. What ?
• Enrich data records and content with semantic tags, free-text keywords or comments
without changing the (meta)data and the (meta)data record
• Manage & Share annotations
• Integrate with data repositories
• Search annotated data
Why ?
• Improve data discoverability with semantics and user-defined annotations
• Retrieve and aggregate heterogeneous files from distributed sources
How it works?
• Easy-to-use annotation client
• Three types of annotations: semantic tag, free-text keyword, comment
• Auto-completion for semantic annotations (Semantic Index)
• Based on W3C Web Annotation data model
How it integrates?
• Integrate client as widget within data service UI (HTML iFrame)
• Interact through RESTful API (Annotation initialization and retrieval)
• Store annotations in centralized annotation store or deploy local store
Contact: Yann Le Franc ylefranc@esciencefactory.com
B2NOTE – A DATA ANNOTATION SERVICE
20. REPOSITORIES SUPPORTING USERS TO ACHIEVE FAIR
Reusable:
R1. (meta)data are richly described with a plurality of accurate and relevant attributes;
R1.1. (meta)data are released with a clear and accessible data usage license;
R1.2. (meta)data are associated with detailed provenance;
R1.3. (meta)data meet domain-relevant community standards;
How?
Help users to apply license as well as to provide detailed
provenance and adopt community standards on their digital
objects (and metadata).
21. REPOSITORIES SUPPORTING USERS TO ACHIEVE FAIR
Reusable:
R1. (meta)data are richly described with a plurality of accurate and relevant attributes;
R1.1. (meta)data are released with a clear and accessible data usage license;
R1.2. (meta)data are associated with detailed provenance;
R1.3. (meta)data meet domain-relevant community standards;
Example
R1.1
R1.2
22. REPOSITORIES SUPPORTING USERS TO ACHIEVE FAIR
Findable:
F1. (meta)data are assigned a globally unique and persistent
identifier;
F2. data are described with rich metadata;
F3. metadata clearly and explicitly include the identifier of the
data it describes;
F4. (meta)data are registered or indexed in a searchable
resource;
Accessible:
A1. (meta)data are retrievable by their identifier using a
standardized communications protocol;
A1.1 the protocol is open, free, and universally
implementable;
A1.2. the protocol allows for an authentication and
authorization procedure, where necessary;
A2. metadata are accessible, even when the data are no longer
available;
Interoperable:
I1. (meta)data use a formal, accessible, shared, and broadly
applicable language for knowledge representation.
I2. (meta)data use vocabularies that follow FAIR principles;
I3. (meta)data include qualified references to other
(meta)data;
Reusable:
R1. (meta)data are richly described with a plurality of accurate and
relevant attributes;
R1.1. (meta)data are released with a clear and accessible data
usage license;
R1.2. (meta)data are associated with detailed provenance;
R1.3. (meta)data meet domain-relevant community
standards;
23. REPOSITORIES - CHALLENGES FOR BEING FAIRER
Repositories have, of course, complete freedom to implement their functionality.
However, we argue that, with a minimal set of agreed upon elements, repositories
could provide a higher level of interoperability among themselves. This would
facilitate indexing of their offered metadata, tools being able to interact with different
repositories, better integration among complementary services (e.g., a data repository
able to integrate with a vocabulary and metadata template repositories to facilitate
metadata definition).
What could be done?
Agree on a common metadata representation;
Agree on (meta)data accessibility APIs
Agree on the adoption of vocabularies containing terms to represent the different types of
digital objects stored in repositories, e.g., datasets, metadata, ontologies, etc.
24. CONTACT INFO
Luiz Bonino
International Technology Coordinator – GO FAIR
Associate Professor BioSemantics – LUMC
E-mail: luiz.bonino@go-fair.org
Skype: luizolavobonino
Web: www.go-fair.org